Eye movements triggered by any sensory stimulus or mental effort are reflex-like. They always consist of 2 intermingled components of variable length: so-called `slow` phases, during which the eye trajectories correct for target or head displacement (visual pursuit, vestibulo-ocular reflex), and fast phases when the eyes saccade to new orbital positions. As a result, typical eye movements have a saw-tooth-like pattern called ocular nystagmus. The type of nystagmus is named after the associated sensory stimulus (e.g. vestibulo-ocular -VOR, optokinetic-OKN, pursuit-PN. . . ).
In the clinical or neurophysiological study of ocular reflexes, it is therefore necessary to first process the eye trajectories to flag and pool the desired `slow` or `fast` response segments. Reflex characterization and parameter estimation can only follow after this first stage of processing. Hence a general-purpose classifier, applicable to any nystagmus and all stimulus patterns is highly desirable.
In the context of eye movements, current classifiers are heavily dependent on human intervention, and are restricted in the range of their application. Most are in-house research tools for a specific application. Typically they either
assume a priori the waveform of the slow-phase segments; as a result it is difficult to apply them in non-linear cases which are typical in clinical patients; or with different stimulus profiles without reprogramming. PA1 use simple eye velocity and/or interval duration criteria to classify segments; hence, are difficult to use at high fast phase rates, and high-amplitude stimulus levels. PA1 or use bandwidth-style criteria to filter out the high-frequency fast phases (this is the approach also used in Raman spectral analysis to remove fluorescence background), which distorts the estimated slow-phase profiles.
The results are highly unsatisfactory, and usually require intensive human viewing and editing. Hence they do not lend themselves to fully automated or real-time applications. Even more recent efforts using fuzzy logic or neural networks to detect patterns are also very limited in their application.
Work has been done on an automated and generalized approach for the classification of eye-movement segments, as published in the paper co-authored by one of Applicants, "Parametric Classification of Segments in Ocular Nystagmus", Claudio Rey and Henrietta Galiana, IEEE Transactions on Biomedical Engineering, Vol. 38, No. Feb. 2, 1991, the content of which is hereby incorporated by reference. This initial work was restricted to the study of the VOR, and often required human intervention to correct for classification errors due to non-linearities or dynamics in the input/output nystagmus process. Also the algorithm usually failed at high nystagmus rates, due to the nature of its filtered indicators, and was restricted to pure gain (scalar) representations in the VOR.
To Applicants' knowledge, an accurate and reliable method for automatic identification of segments of signals having at least two temporally separate interleaved dominant components is not known in the art of signal processing. Such a method would be useful not only in the biomedical application of analyzing nystagmus signals, but also in analyzing many other similar signals, such as Raman spectra signals having a fluorescence background signal.